├── tmp_task.py ├── stream ├── __init__.py ├── __pycache__ │ ├── urls.cpython-35.pyc │ ├── urls.cpython-36.pyc │ ├── urls.cpython-37.pyc │ ├── wsgi.cpython-35.pyc │ ├── wsgi.cpython-36.pyc │ ├── wsgi.cpython-37.pyc │ ├── __init__.cpython-35.pyc │ ├── __init__.cpython-36.pyc │ ├── __init__.cpython-37.pyc │ ├── settings.cpython-35.pyc │ ├── settings.cpython-36.pyc │ └── settings.cpython-37.pyc ├── wsgi.py ├── urls.py └── settings.py ├── yolov3_weight └── put_yolo_weight_here.txt ├── webcam ├── models.py ├── tests.py ├── admin.py ├── apps.py ├── templates │ ├── images │ │ ├── japan.jpg │ │ ├── ocst.jpg │ │ ├── team │ │ │ ├── 1.jpg │ │ │ ├── 2.jpg │ │ │ └── 3.jpg │ │ ├── fujifilm.jpg │ │ ├── puppydog.jpg │ │ ├── wooden-desk.jpg │ │ ├── gutman-island.jpg │ │ ├── testimonial01.jpg │ │ ├── testimonial02.jpg │ │ ├── testimonial03.jpg │ │ └── testimonial04.jpg │ ├── fonts │ │ ├── FontAwesome.otf │ │ ├── fontawesome-webfont.eot │ │ ├── fontawesome-webfont.ttf │ │ └── fontawesome-webfont.woff │ ├── camera2.html │ ├── index.html │ ├── camera1.html │ └── css │ │ ├── templatemo_style.css │ │ └── font-awesome.min.css ├── __pycache__ │ ├── apps.cpython-37.pyc │ ├── admin.cpython-35.pyc │ ├── admin.cpython-36.pyc │ ├── admin.cpython-37.pyc │ ├── models.cpython-35.pyc │ ├── models.cpython-36.pyc │ ├── models.cpython-37.pyc │ ├── views.cpython-35.pyc │ ├── views.cpython-36.pyc │ ├── views.cpython-37.pyc │ ├── __init__.cpython-35.pyc │ └── __init__.cpython-36.pyc ├── migrations │ └── __pycache__ │ │ ├── __init__.cpython-35.pyc │ │ └── __init__.cpython-36.pyc └── views.py ├── images ├── cam1.png ├── cam2.png └── home.png ├── data ├── anchors │ ├── coco_anchors.txt │ └── basline_anchors.txt ├── classes │ ├── voc.names │ └── coco.names └── dataset │ ├── voc_test.txt │ └── voc_train.txt ├── static ├── images │ ├── japan.jpg │ ├── ocst.jpg │ ├── team │ │ ├── 1.jpg │ │ ├── 2.jpg │ │ └── 3.jpg │ ├── fujifilm.jpg │ ├── puppydog.jpg │ ├── wooden-desk.jpg │ ├── gutman-island.jpg │ ├── testimonial01.jpg │ ├── testimonial02.jpg │ ├── testimonial03.jpg │ └── testimonial04.jpg └── css │ ├── templatemo_style.css │ └── font-awesome.min.css ├── core ├── __pycache__ │ ├── common.cpython-36.pyc │ ├── config.cpython-36.pyc │ ├── utils.cpython-36.pyc │ ├── yolov3.cpython-36.pyc │ ├── __init__.cpython-36.pyc │ └── backbone.cpython-36.pyc ├── config.py ├── backbone.py ├── common.py ├── utils.py ├── dataset.py └── yolov3.py ├── manage.py ├── requirements.txt ├── LICENSE ├── README.md ├── webcam_detection.py └── train.py /tmp_task.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /stream/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /yolov3_weight/put_yolo_weight_here.txt: -------------------------------------------------------------------------------- 1 | put_yolo_weight_here!!! -------------------------------------------------------------------------------- /webcam/models.py: -------------------------------------------------------------------------------- 1 | from django.db import models 2 | 3 | # Create your models here. 4 | -------------------------------------------------------------------------------- /webcam/tests.py: -------------------------------------------------------------------------------- 1 | from django.test import TestCase 2 | 3 | # Create your tests here. 4 | -------------------------------------------------------------------------------- /webcam/admin.py: -------------------------------------------------------------------------------- 1 | from django.contrib import admin 2 | 3 | # Register your models here. 4 | -------------------------------------------------------------------------------- /images/cam1.png: 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3.875,2.8125, 3.6875,7.4375, 3.625,2.8125, 4.875,6.1875, 11.65625,10.1875 2 | -------------------------------------------------------------------------------- /webcam/templates/fonts/fontawesome-webfont.eot: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tranleanh/yolo-django-streaming/HEAD/webcam/templates/fonts/fontawesome-webfont.eot -------------------------------------------------------------------------------- /webcam/templates/fonts/fontawesome-webfont.ttf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tranleanh/yolo-django-streaming/HEAD/webcam/templates/fonts/fontawesome-webfont.ttf -------------------------------------------------------------------------------- /webcam/templates/fonts/fontawesome-webfont.woff: -------------------------------------------------------------------------------- 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aeroplane 2 | bicycle 3 | bird 4 | boat 5 | bottle 6 | bus 7 | car 8 | cat 9 | chair 10 | cow 11 | diningtable 12 | dog 13 | horse 14 | motorbike 15 | person 16 | pottedplant 17 | sheep 18 | sofa 19 | train 20 | tvmonitor -------------------------------------------------------------------------------- /stream/wsgi.py: -------------------------------------------------------------------------------- 1 | """ 2 | WSGI config for stream project. 3 | 4 | It exposes the WSGI callable as a module-level variable named ``application``. 5 | 6 | For more information on this file, see 7 | https://docs.djangoproject.com/en/2.2/howto/deployment/wsgi/ 8 | """ 9 | 10 | import os 11 | 12 | from django.core.wsgi import get_wsgi_application 13 | 14 | os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'stream.settings') 15 | 16 | application = get_wsgi_application() 17 | -------------------------------------------------------------------------------- /manage.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | """Django's command-line utility for administrative tasks.""" 3 | import os 4 | import sys 5 | 6 | 7 | def main(): 8 | os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'stream.settings') 9 | try: 10 | from django.core.management import execute_from_command_line 11 | except ImportError as exc: 12 | raise ImportError( 13 | "Couldn't import Django. Are you sure it's installed and " 14 | "available on your PYTHONPATH environment variable? Did you " 15 | "forget to activate a virtual environment?" 16 | ) from exc 17 | execute_from_command_line(sys.argv) 18 | 19 | 20 | if __name__ == '__main__': 21 | main() 22 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | absl-py==0.13.0 2 | asgiref==3.4.1 3 | astor==0.8.1 4 | cached-property==1.5.2 5 | Django==3.2.5 6 | easydict==1.9 7 | gast==0.2.2 8 | google-pasta==0.2.0 9 | grpcio==1.39.0 10 | h5py==3.3.0 11 | importlib-metadata==4.6.1 12 | Keras-Applications==1.0.8 13 | Keras-Preprocessing==1.1.2 14 | Markdown==3.3.4 15 | mysqlclient==2.0.3 16 | numpy==1.21.1 17 | opencv-python==4.5.3.56 18 | opt-einsum==3.3.0 19 | Pillow==8.3.1 20 | protobuf==3.17.3 21 | pytz==2021.1 22 | six==1.16.0 23 | sqlparse==0.4.1 24 | tensorboard==1.15.0 25 | tensorflow==1.15.0 26 | tensorflow-estimator==1.15.1 27 | termcolor==1.1.0 28 | typing-extensions==3.10.0.0 29 | Werkzeug==2.0.1 30 | wrapt==1.12.1 31 | zipp==3.5.0 32 | -------------------------------------------------------------------------------- /data/classes/coco.names: -------------------------------------------------------------------------------- 1 | person 2 | bicycle 3 | car 4 | motorbike 5 | aeroplane 6 | bus 7 | train 8 | truck 9 | boat 10 | traffic light 11 | fire hydrant 12 | stop sign 13 | parking meter 14 | bench 15 | bird 16 | cat 17 | dog 18 | horse 19 | sheep 20 | cow 21 | elephant 22 | bear 23 | zebra 24 | giraffe 25 | backpack 26 | umbrella 27 | handbag 28 | tie 29 | suitcase 30 | frisbee 31 | skis 32 | snowboard 33 | sports ball 34 | kite 35 | baseball bat 36 | baseball glove 37 | skateboard 38 | surfboard 39 | tennis racket 40 | bottle 41 | wine glass 42 | cup 43 | fork 44 | knife 45 | spoon 46 | bowl 47 | banana 48 | apple 49 | sandwich 50 | orange 51 | broccoli 52 | carrot 53 | hot dog 54 | pizza 55 | donut 56 | cake 57 | chair 58 | sofa 59 | pottedplant 60 | bed 61 | diningtable 62 | toilet 63 | tvmonitor 64 | laptop 65 | mouse 66 | remote 67 | keyboard 68 | cell phone 69 | microwave 70 | oven 71 | toaster 72 | sink 73 | refrigerator 74 | book 75 | clock 76 | vase 77 | scissors 78 | teddy bear 79 | hair drier 80 | toothbrush 81 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 LA Tran 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /stream/urls.py: -------------------------------------------------------------------------------- 1 | """stream URL Configuration 2 | 3 | The `urlpatterns` list routes URLs to views. For more information please see: 4 | https://docs.djangoproject.com/en/2.2/topics/http/urls/ 5 | Examples: 6 | Function views 7 | 1. Add an import: from my_app import views 8 | 2. Add a URL to urlpatterns: path('', views.home, name='home') 9 | Class-based views 10 | 1. Add an import: from other_app.views import Home 11 | 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') 12 | Including another URLconf 13 | 1. Import the include() function: from django.urls import include, path 14 | 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) 15 | """ 16 | # 2019-07-23 Modified by Tran Le Anh 17 | 18 | from django.contrib import admin 19 | from django.urls import path 20 | from webcam.views import index, video_feed_1, video_feed_2, camera_1, camera_2 21 | # from webcam.views import database, search 22 | 23 | urlpatterns = [ 24 | path('admin/', admin.site.urls), 25 | path('index/', index), 26 | path('video_feed_1/', video_feed_1, name="video-feed-1"), 27 | path('video_feed_2/', video_feed_2, name="video-feed-2"), 28 | path('index/camera1/', camera_1), 29 | path('index/camera2/', camera_2), 30 | # path('index/database/', database), 31 | # path('index/database/50latest', database), 32 | # path('index/database/search', search), 33 | ] 34 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # yolov3-django-streaming 2 | 3 | This project is to stream object detection (yolov3) with 2 cameras (2 switchable channels) on web browser using Django framework. 4 | 5 | The project can be deployed on Ubuntu and Windows. 6 | 7 | Watch final result: https://www.youtube.com/watch?v=SDnpNd7xRbE&t=10s 8 | 9 | ## 1. Steps to use 10 | 1. Download file "yolov3_coco.pb" from the link below and locate it in folder "yolov3_weight": ([gotolink](https://drive.google.com/drive/u/1/folders/1apB-yPIxxzC9D6_iAaQrXWuGpbWIK6Lp)) 11 | ```bashrc 12 | https://drive.google.com/drive/u/1/folders/1apB-yPIxxzC9D6_iAaQrXWuGpbWIK6Lp 13 | ``` 14 | 2. Create your virtual environment and install required packages: 15 | ```bashrc 16 | $ pip install -r requirements.txt 17 | ``` 18 | 3. Run program: 19 | ```bashrc 20 | $ python manage.py runserver 21 | ``` 22 | 4. Open any web browser and navigate to URL (home page): 23 | ```bashrc 24 | http://127.0.0.1:8000/index 25 | ``` 26 | ## 2. What should happen then? 27 | (don't worry, my face will not be on your screen) 28 | - Camera 1: 29 | 30 | ![picture](images/cam1.png) 31 | 32 | - Camera 2: 33 | 34 | ![picture](images/cam2.png) 35 | 36 | ## 3. Be careful 37 | - The 2 camera ids in the source code are "0" and "2" (for my computer). 38 | - You should change them for running on any other computers. Go to webcam/views.py then find and change "cam_id" parameter. 39 | 40 | 41 | ## Acknowledgement 42 | - The yolov3 implementation was borrowed from [YunYang1994](https://github.com/YunYang1994/tensorflow-yolov3) 43 | 44 | 45 | ## Cite This Project 46 | ```bashrc 47 | @article{tran2020yolostream, 48 | title={Object Detection Streaming and Data Management on Web Browser}, 49 | author={Tran, Le-Anh}, 50 | journal={Technical Report}, 51 | year={2020} 52 | } 53 | ``` 54 | 55 | Good luck. 56 | 57 | Created on July 11, 2019. 58 | 59 | Last update on July 22, 2021. (because I had received tons of emails for fixing this project, thanks) 60 | 61 | Tran Le Anh ([LA Tran](https://sites.google.com/view/leanhtran/)) 62 | 63 | -------------------------------------------------------------------------------- /data/dataset/voc_test.txt: -------------------------------------------------------------------------------- 1 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000001.jpg 48,240,195,371,11 8,12,352,498,14 2 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000002.jpg 139,200,207,301,18 3 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000003.jpg 123,155,215,195,17 239,156,307,205,8 4 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000004.jpg 13,311,84,362,6 362,330,500,389,6 235,328,334,375,6 175,327,252,364,6 139,320,189,359,6 108,325,150,353,6 84,323,121,350,6 5 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000006.jpg 187,135,282,242,15 154,209,369,375,10 255,207,366,375,8 138,211,249,375,8 6 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000008.jpg 192,16,364,249,8 7 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000010.jpg 87,97,258,427,12 133,72,245,284,14 8 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000011.jpg 126,51,330,308,7 9 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000013.jpg 299,160,446,252,9 10 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000014.jpg 72,163,302,228,5 185,194,500,316,6 416,180,500,222,6 314,8,344,65,14 331,4,361,61,14 357,8,401,61,14 11 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000015.jpg 77,136,360,358,1 12 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000018.jpg 31,30,358,279,11 13 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000022.jpg 68,103,368,283,12 186,44,255,230,14 14 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000025.jpg 2,84,59,248,9 68,115,233,279,9 64,173,377,373,9 320,2,496,375,14 221,4,341,374,14 135,14,220,148,14 69,43,156,177,9 58,54,104,139,14 279,1,331,86,14 320,22,344,96,14 15 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000027.jpg 174,101,349,351,14 16 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000028.jpg 63,18,374,500,7 17 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000029.jpg 56,63,284,290,11 18 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000031.jpg 41,77,430,255,18 19 | /home/yang/test/VOC/test/VOCdevkit/VOC2007/JPEGImages/000037.jpg 61,96,464,339,11 20 | -------------------------------------------------------------------------------- /data/dataset/voc_train.txt: -------------------------------------------------------------------------------- 1 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000005.jpg 263,211,324,339,8 165,264,253,372,8 241,194,295,299,8 2 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000007.jpg 141,50,500,330,6 3 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000009.jpg 69,172,270,330,12 150,141,229,284,14 285,201,327,331,14 258,198,297,329,14 4 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000012.jpg 156,97,351,270,6 5 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000016.jpg 92,72,305,473,1 6 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000017.jpg 185,62,279,199,14 90,78,403,336,12 7 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000019.jpg 231,88,483,256,7 11,113,266,259,7 8 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000020.jpg 33,148,371,416,6 9 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000021.jpg 1,235,182,388,11 210,36,336,482,14 46,82,170,365,14 11,181,142,419,14 10 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000023.jpg 9,230,245,500,1 230,220,334,500,1 2,1,117,369,14 3,2,243,462,14 225,1,334,486,14 11 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000024.jpg 196,165,489,247,18 12 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000026.jpg 90,125,337,212,6 13 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000030.jpg 36,205,180,289,1 51,160,150,292,14 295,138,450,290,14 14 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000032.jpg 104,78,375,183,0 133,88,197,123,0 195,180,213,229,14 26,189,44,238,14 15 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000033.jpg 9,107,499,263,0 421,200,482,226,0 325,188,411,223,0 16 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000034.jpg 116,167,360,400,18 141,153,333,229,18 17 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000035.jpg 1,96,191,361,14 218,98,465,318,14 18 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000036.jpg 27,79,319,344,11 19 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000039.jpg 156,89,344,279,19 20 | /home/yang/test/VOC/train/VOCdevkit/VOC2007/JPEGImages/000041.jpg 363,47,432,107,19 216,92,307,302,14 164,148,227,244,14 21 | -------------------------------------------------------------------------------- /core/config.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | # coding=utf-8 3 | #================================================================ 4 | # Copyright (C) 2019 * Ltd. All rights reserved. 5 | # 6 | # Editor : VIM 7 | # File name : config.py 8 | # Author : YunYang1994 9 | # Created date: 2019-02-28 13:06:54 10 | # Description : 11 | # 12 | #================================================================ 13 | 14 | from easydict import EasyDict as edict 15 | 16 | 17 | __C = edict() 18 | # Consumers can get config by: from config import cfg 19 | 20 | cfg = __C 21 | 22 | # YOLO options 23 | __C.YOLO = edict() 24 | 25 | # Set the class name 26 | __C.YOLO.CLASSES = "./data/classes/coco.names" 27 | __C.YOLO.ANCHORS = "./data/anchors/basline_anchors.txt" 28 | __C.YOLO.MOVING_AVE_DECAY = 0.9995 29 | __C.YOLO.STRIDES = [8, 16, 32] 30 | __C.YOLO.ANCHOR_PER_SCALE = 3 31 | __C.YOLO.IOU_LOSS_THRESH = 0.5 32 | __C.YOLO.UPSAMPLE_METHOD = "resize" 33 | __C.YOLO.ORIGINAL_WEIGHT = "./checkpoint/yolov3_coco.ckpt" 34 | __C.YOLO.DEMO_WEIGHT = "./checkpoint/yolov3_coco_demo.ckpt" 35 | 36 | # Train options 37 | __C.TRAIN = edict() 38 | 39 | __C.TRAIN.ANNOT_PATH = "./data/dataset/voc_train.txt" 40 | __C.TRAIN.BATCH_SIZE = 6 41 | __C.TRAIN.INPUT_SIZE = [320, 352, 384, 416, 448, 480, 512, 544, 576, 608] 42 | __C.TRAIN.DATA_AUG = True 43 | __C.TRAIN.LEARN_RATE_INIT = 1e-4 44 | __C.TRAIN.LEARN_RATE_END = 1e-6 45 | __C.TRAIN.WARMUP_EPOCHS = 2 46 | __C.TRAIN.FISRT_STAGE_EPOCHS = 20 47 | __C.TRAIN.SECOND_STAGE_EPOCHS = 30 48 | __C.TRAIN.INITIAL_WEIGHT = "./checkpoint/yolov3_coco_demo.ckpt" 49 | 50 | 51 | 52 | # TEST options 53 | __C.TEST = edict() 54 | 55 | __C.TEST.ANNOT_PATH = "./data/dataset/voc_test.txt" 56 | __C.TEST.BATCH_SIZE = 2 57 | __C.TEST.INPUT_SIZE = 544 58 | __C.TEST.DATA_AUG = False 59 | __C.TEST.WRITE_IMAGE = True 60 | __C.TEST.WRITE_IMAGE_PATH = "./data/detection/" 61 | __C.TEST.WRITE_IMAGE_SHOW_LABEL = False 62 | __C.TEST.WEIGHT_FILE = "./checkpoint/yolov3_test_loss=9.2099.ckpt-5" 63 | __C.TEST.SHOW_LABEL = False 64 | __C.TEST.SCORE_THRESHOLD = 0.3 65 | __C.TEST.IOU_THRESHOLD = 0.45 66 | 67 | 68 | 69 | 70 | 71 | 72 | -------------------------------------------------------------------------------- /core/backbone.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | # coding=utf-8 3 | #================================================================ 4 | # Copyright (C) 2019 * Ltd. All rights reserved. 5 | # 6 | # Editor : VIM 7 | # File name : backbone.py 8 | # Author : YunYang1994 9 | # Created date: 2019-02-17 11:03:35 10 | # Description : 11 | # 12 | #================================================================ 13 | 14 | import core.common as common 15 | import tensorflow as tf 16 | 17 | 18 | def darknet53(input_data, trainable): 19 | 20 | with tf.variable_scope('darknet'): 21 | 22 | input_data = common.convolutional(input_data, filters_shape=(3, 3, 3, 32), trainable=trainable, name='conv0') 23 | input_data = common.convolutional(input_data, filters_shape=(3, 3, 32, 64), 24 | trainable=trainable, name='conv1', downsample=True) 25 | 26 | for i in range(1): 27 | input_data = common.residual_block(input_data, 64, 32, 64, trainable=trainable, name='residual%d' %(i+0)) 28 | 29 | input_data = common.convolutional(input_data, filters_shape=(3, 3, 64, 128), 30 | trainable=trainable, name='conv4', downsample=True) 31 | 32 | for i in range(2): 33 | input_data = common.residual_block(input_data, 128, 64, 128, trainable=trainable, name='residual%d' %(i+1)) 34 | 35 | input_data = common.convolutional(input_data, filters_shape=(3, 3, 128, 256), 36 | trainable=trainable, name='conv9', downsample=True) 37 | 38 | for i in range(8): 39 | input_data = common.residual_block(input_data, 256, 128, 256, trainable=trainable, name='residual%d' %(i+3)) 40 | 41 | route_1 = input_data 42 | input_data = common.convolutional(input_data, filters_shape=(3, 3, 256, 512), 43 | trainable=trainable, name='conv26', downsample=True) 44 | 45 | for i in range(8): 46 | input_data = common.residual_block(input_data, 512, 256, 512, trainable=trainable, name='residual%d' %(i+11)) 47 | 48 | route_2 = input_data 49 | input_data = common.convolutional(input_data, filters_shape=(3, 3, 512, 1024), 50 | trainable=trainable, name='conv43', downsample=True) 51 | 52 | for i in range(4): 53 | input_data = common.residual_block(input_data, 1024, 512, 1024, trainable=trainable, name='residual%d' %(i+19)) 54 | 55 | return route_1, route_2, input_data 56 | 57 | 58 | 59 | 60 | -------------------------------------------------------------------------------- /webcam/templates/camera2.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 8 | Camera 2 9 | 10 | 11 | 12 | 13 | 14 | 15 | {% load static %} 16 | 17 | 18 | 19 | 20 | 21 | 22 | 30 |
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72 | 73 | -------------------------------------------------------------------------------- /webcam_detection.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import time 3 | import numpy as np 4 | import core.utils as utils 5 | import tensorflow as tf 6 | from PIL import Image 7 | 8 | obj_classes = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"] 9 | 10 | return_elements = ["input/input_data:0", "pred_sbbox/concat_2:0", "pred_mbbox/concat_2:0", "pred_lbbox/concat_2:0"] 11 | pb_file = "./yolov3_coco.pb" 12 | # video_path = "./docs/images/road.mp4" 13 | video_path = 0 14 | num_classes = 80 15 | input_size = 416 16 | graph = tf.Graph() 17 | return_tensors = utils.read_pb_return_tensors(graph, pb_file, return_elements) 18 | 19 | vid = cv2.VideoCapture(video_path) 20 | 21 | def detection(vid): 22 | with tf.Session(graph=graph) as sess: 23 | 24 | return_value, frame = vid.read() 25 | if return_value: 26 | frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) 27 | image = Image.fromarray(frame) 28 | else: 29 | raise ValueError("No image!") 30 | 31 | 32 | frame_size = frame.shape[:2] 33 | image_data = utils.image_preporcess(np.copy(frame), [input_size, input_size]) 34 | image_data = image_data[np.newaxis, ...] 35 | prev_time = time.time() 36 | 37 | pred_sbbox, pred_mbbox, pred_lbbox = sess.run( 38 | [return_tensors[1], return_tensors[2], return_tensors[3]], 39 | feed_dict={ return_tensors[0]: image_data}) 40 | 41 | pred_bbox = np.concatenate([np.reshape(pred_sbbox, (-1, 5 + num_classes)), 42 | np.reshape(pred_mbbox, (-1, 5 + num_classes)), 43 | np.reshape(pred_lbbox, (-1, 5 + num_classes))], axis=0) 44 | 45 | bboxes = utils.postprocess_boxes(pred_bbox, frame_size, input_size, 0.3) 46 | bboxes = utils.nms(bboxes, 0.45, method='nms') 47 | image, detected = utils.draw_bbox(frame, bboxes) 48 | 49 | 50 | detected = np.asarray(detected) 51 | 52 | print("------- frame i ---------") 53 | 54 | class_count = [] 55 | 56 | for i in range(len(obj_classes)): # 80 57 | obj_count = 0 58 | for j in range(len(detected)): 59 | if int(detected[j][5]) == i: obj_count += 1 60 | 61 | class_count = np.append(class_count, obj_count) 62 | 63 | 64 | 65 | curr_time = time.time() 66 | exec_time = curr_time - prev_time 67 | result = np.asarray(image) 68 | info = "time: %.2f ms" %(1000*exec_time) 69 | # cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE) 70 | result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) 71 | 72 | return result, class_count 73 | 74 | # if __name__ == "__main__": 75 | 76 | # while True: 77 | # result = detection(vid) 78 | 79 | # cv2.imshow("result", result) 80 | # if cv2.waitKey(1) & 0xFF == ord('q'): break -------------------------------------------------------------------------------- /core/common.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | # coding=utf-8 3 | #================================================================ 4 | # Copyright (C) 2019 * Ltd. All rights reserved. 5 | # 6 | # Editor : VIM 7 | # File name : common.py 8 | # Author : YunYang1994 9 | # Created date: 2019-02-28 09:56:29 10 | # Description : 11 | # 12 | #================================================================ 13 | 14 | import tensorflow as tf 15 | 16 | 17 | def convolutional(input_data, filters_shape, trainable, name, downsample=False, activate=True, bn=True): 18 | 19 | with tf.variable_scope(name): 20 | if downsample: 21 | pad_h, pad_w = (filters_shape[0] - 2) // 2 + 1, (filters_shape[1] - 2) // 2 + 1 22 | paddings = tf.constant([[0, 0], [pad_h, pad_h], [pad_w, pad_w], [0, 0]]) 23 | input_data = tf.pad(input_data, paddings, 'CONSTANT') 24 | strides = (1, 2, 2, 1) 25 | padding = 'VALID' 26 | else: 27 | strides = (1, 1, 1, 1) 28 | padding = "SAME" 29 | 30 | weight = tf.get_variable(name='weight', dtype=tf.float32, trainable=True, 31 | shape=filters_shape, initializer=tf.random_normal_initializer(stddev=0.01)) 32 | conv = tf.nn.conv2d(input=input_data, filter=weight, strides=strides, padding=padding) 33 | 34 | if bn: 35 | conv = tf.layers.batch_normalization(conv, beta_initializer=tf.zeros_initializer(), 36 | gamma_initializer=tf.ones_initializer(), 37 | moving_mean_initializer=tf.zeros_initializer(), 38 | moving_variance_initializer=tf.ones_initializer(), training=trainable) 39 | else: 40 | bias = tf.get_variable(name='bias', shape=filters_shape[-1], trainable=True, 41 | dtype=tf.float32, initializer=tf.constant_initializer(0.0)) 42 | conv = tf.nn.bias_add(conv, bias) 43 | 44 | if activate == True: conv = tf.nn.leaky_relu(conv, alpha=0.1) 45 | 46 | return conv 47 | 48 | 49 | def residual_block(input_data, input_channel, filter_num1, filter_num2, trainable, name): 50 | 51 | short_cut = input_data 52 | 53 | with tf.variable_scope(name): 54 | input_data = convolutional(input_data, filters_shape=(1, 1, input_channel, filter_num1), 55 | trainable=trainable, name='conv1') 56 | input_data = convolutional(input_data, filters_shape=(3, 3, filter_num1, filter_num2), 57 | trainable=trainable, name='conv2') 58 | 59 | residual_output = input_data + short_cut 60 | 61 | return residual_output 62 | 63 | 64 | 65 | def route(name, previous_output, current_output): 66 | 67 | with tf.variable_scope(name): 68 | output = tf.concat([current_output, previous_output], axis=-1) 69 | 70 | return output 71 | 72 | 73 | def upsample(input_data, name, method="deconv"): 74 | assert method in ["resize", "deconv"] 75 | 76 | if method == "resize": 77 | with tf.variable_scope(name): 78 | input_shape = tf.shape(input_data) 79 | output = tf.image.resize_nearest_neighbor(input_data, (input_shape[1] * 2, input_shape[2] * 2)) 80 | 81 | if method == "deconv": 82 | # replace resize_nearest_neighbor with conv2d_transpose To support TensorRT optimization 83 | numm_filter = input_data.shape.as_list()[-1] 84 | output = tf.layers.conv2d_transpose(input_data, numm_filter, kernel_size=2, padding='same', 85 | strides=(2,2), kernel_initializer=tf.random_normal_initializer()) 86 | 87 | return output 88 | 89 | 90 | 91 | -------------------------------------------------------------------------------- /stream/settings.py: -------------------------------------------------------------------------------- 1 | """ 2 | Django settings for stream project. 3 | 4 | Generated by 'django-admin startproject' using Django 2.2.3. 5 | 6 | For more information on this file, see 7 | https://docs.djangoproject.com/en/2.2/topics/settings/ 8 | 9 | For the full list of settings and their values, see 10 | https://docs.djangoproject.com/en/2.2/ref/settings/ 11 | """ 12 | 13 | # 2019-07-23 Modified by Tran Le Anh 14 | 15 | import os 16 | 17 | # Build paths inside the project like this: os.path.join(BASE_DIR, ...) 18 | BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) 19 | 20 | 21 | # Quick-start development settings - unsuitable for production 22 | # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ 23 | 24 | # SECURITY WARNING: keep the secret key used in production secret! 25 | SECRET_KEY = 'os*hqvscy)s#9m!u0kcr-^_$)8@upg94&-bi9oso*=rrvx6$&x' 26 | 27 | # SECURITY WARNING: don't run with debug turned on in production! 28 | DEBUG = True 29 | 30 | ALLOWED_HOSTS = ['192.168.11.232', '127.0.0.1'] 31 | 32 | 33 | # Application definition 34 | 35 | INSTALLED_APPS = [ 36 | 'django.contrib.admin', 37 | 'django.contrib.auth', 38 | 'django.contrib.contenttypes', 39 | 'django.contrib.sessions', 40 | 'django.contrib.messages', 41 | 'django.contrib.staticfiles', 42 | 'webcam' 43 | ] 44 | 45 | MIDDLEWARE = [ 46 | 'django.middleware.security.SecurityMiddleware', 47 | 'django.contrib.sessions.middleware.SessionMiddleware', 48 | 'django.middleware.common.CommonMiddleware', 49 | 'django.middleware.csrf.CsrfViewMiddleware', 50 | 'django.contrib.auth.middleware.AuthenticationMiddleware', 51 | 'django.contrib.messages.middleware.MessageMiddleware', 52 | 'django.middleware.clickjacking.XFrameOptionsMiddleware', 53 | ] 54 | 55 | ROOT_URLCONF = 'stream.urls' 56 | 57 | TEMPLATES = [ 58 | { 59 | 'BACKEND': 'django.template.backends.django.DjangoTemplates', 60 | 'DIRS': [], 61 | # 'DIRS': [os.path.join(BASE_DIR, 'templates')], 62 | 'APP_DIRS': True, 63 | 'OPTIONS': { 64 | 'context_processors': [ 65 | 'django.template.context_processors.debug', 66 | 'django.template.context_processors.request', 67 | 'django.contrib.auth.context_processors.auth', 68 | 'django.contrib.messages.context_processors.messages', 69 | ], 70 | }, 71 | }, 72 | ] 73 | 74 | WSGI_APPLICATION = 'stream.wsgi.application' 75 | 76 | 77 | # Database 78 | # https://docs.djangoproject.com/en/2.2/ref/settings/#databases 79 | 80 | # DATABASES = { 81 | # # 'default': { 82 | # # 'ENGINE': 'django.db.backends.sqlite3', 83 | # # 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), 84 | # # } 85 | 86 | # 'default': { 87 | # # MySQL database engine class. 88 | # 'ENGINE': 'django.db.backends.mysql', 89 | # # MySQL database host ip. 90 | # 'HOST': '127.0.0.1', 91 | # # port number. 92 | # 'PORT': '3306', 93 | # # database name. 94 | # 'NAME': 'ocst', 95 | # # user name. 96 | # 'USER': 'root', 97 | # # password 98 | # 'PASSWORD': 'password', 99 | # # connect options 100 | # 'OPTIONS': {'init_command': "SET sql_mode='STRICT_TRANS_TABLES'",}, 101 | # } 102 | # } 103 | 104 | 105 | # Password validation 106 | # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators 107 | 108 | AUTH_PASSWORD_VALIDATORS = [ 109 | { 110 | 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', 111 | }, 112 | { 113 | 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', 114 | }, 115 | { 116 | 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', 117 | }, 118 | { 119 | 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', 120 | }, 121 | ] 122 | 123 | 124 | # Internationalization 125 | # https://docs.djangoproject.com/en/2.2/topics/i18n/ 126 | 127 | LANGUAGE_CODE = 'en-us' 128 | 129 | # TIME_ZONE = 'UTC' 130 | TIME_ZONE = 'Asia/Seoul' 131 | 132 | USE_I18N = True 133 | 134 | USE_L10N = True 135 | 136 | # USE_TZ = True 137 | USE_TZ = False 138 | 139 | 140 | # Static files (CSS, JavaScript, Images) 141 | # https://docs.djangoproject.com/en/2.2/howto/static-files/ 142 | 143 | STATIC_URL = '/static/' 144 | 145 | STATICFILES_DIRS = [ 146 | os.path.join(BASE_DIR, "static"), 147 | ] 148 | -------------------------------------------------------------------------------- /webcam/views.py: -------------------------------------------------------------------------------- 1 | from django.shortcuts import render 2 | 3 | # Create your views here. 4 | from django.http import HttpResponse 5 | from django.template import RequestContext, loader 6 | from django.http.response import StreamingHttpResponse 7 | 8 | import cv2 9 | import numpy as np 10 | import datetime 11 | import time 12 | 13 | import core.utils as utils 14 | import tensorflow as tf 15 | from PIL import Image 16 | 17 | # HOME PAGE ------------------------- 18 | def index(request): 19 | template = loader.get_template('index.html') 20 | return HttpResponse(template.render({}, request)) 21 | # ----------------------------------- 22 | 23 | # CAMERA 1 PAGE --------------------- 24 | def camera_1(request): 25 | template = loader.get_template('camera1.html') 26 | return HttpResponse(template.render({}, request)) 27 | # ----------------------------------- 28 | 29 | # CAMERA 1 PAGE --------------------- 30 | def camera_2(request): 31 | template = loader.get_template('camera2.html') 32 | return HttpResponse(template.render({}, request)) 33 | # ----------------------------------- 34 | 35 | # DISPLAY CAMERA 1 ------------------ 36 | def stream_1(): 37 | 38 | cam_id = 0 39 | vid = cv2.VideoCapture(cam_id) 40 | 41 | while True: 42 | frame, class_count = detection(vid) 43 | 44 | frame = cv2.resize(frame, (1000, 700)) 45 | 46 | print("\nObjects in frame:") 47 | row = 0 48 | for k in range(len(class_count)): 49 | if class_count[k] > 0: 50 | row += 1 51 | infor = str(obj_classes[k]) + ": " + str(int(class_count[k])) 52 | print(" " + infor) 53 | frame = cv2.putText(frame,infor,(20,(row+1)*35), cv2.FONT_HERSHEY_SIMPLEX, 1,(255,255,255),2,cv2.LINE_AA) 54 | 55 | cv2.imwrite('currentframe.jpg', frame) 56 | yield (b'--frame\r\n' 57 | b'Content-Type: image/jpeg\r\n\r\n' + open('currentframe.jpg', 'rb').read() + b'\r\n') 58 | 59 | def video_feed_1(request): 60 | return StreamingHttpResponse(stream_1(), content_type='multipart/x-mixed-replace; boundary=frame') 61 | # ----------------------------------- 62 | 63 | 64 | # DISPLAY CAMERA 2 ------------------ 65 | def stream_2(): 66 | 67 | cam_id = 2 68 | vid = cv2.VideoCapture(cam_id) 69 | 70 | while True: 71 | frame, class_count = detection(vid) 72 | 73 | frame = cv2.resize(frame, (1000, 700)) 74 | 75 | print("\nObjects in frame:") 76 | row = 0 77 | for k in range(len(class_count)): 78 | if class_count[k] > 0: 79 | row += 1 80 | infor = str(obj_classes[k]) + ": " + str(int(class_count[k])) 81 | print(" " + infor) 82 | frame = cv2.putText(frame,infor,(20,(row+1)*35), cv2.FONT_HERSHEY_SIMPLEX, 1,(255,255,255),2,cv2.LINE_AA) 83 | 84 | cv2.imwrite('currentframe.jpg', frame) 85 | yield (b'--frame\r\n' 86 | b'Content-Type: image/jpeg\r\n\r\n' + open('currentframe.jpg', 'rb').read() + b'\r\n') 87 | 88 | def video_feed_2(request): 89 | return StreamingHttpResponse(stream_2(), content_type='multipart/x-mixed-replace; boundary=frame') 90 | # ----------------------------------- 91 | 92 | # PARAMETERS FOR YOLO---------------- 93 | obj_classes = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"] 94 | 95 | return_elements = ["input/input_data:0", "pred_sbbox/concat_2:0", "pred_mbbox/concat_2:0", "pred_lbbox/concat_2:0"] 96 | pb_file = "./yolov3_weight/yolov3_coco.pb" 97 | num_classes = 80 98 | input_size = 416 99 | graph = tf.Graph() 100 | return_tensors = utils.read_pb_return_tensors(graph, pb_file, return_elements) 101 | # ----------------------------------- 102 | 103 | # YOLO DETECTION -------------------- 104 | def detection(vid): 105 | with tf.Session(graph=graph) as sess: 106 | 107 | return_value, frame = vid.read() 108 | if return_value: 109 | frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) 110 | image = Image.fromarray(frame) 111 | else: 112 | raise ValueError("No image!") 113 | 114 | 115 | frame_size = frame.shape[:2] 116 | image_data = utils.image_preporcess(np.copy(frame), [input_size, input_size]) 117 | image_data = image_data[np.newaxis, ...] 118 | prev_time = time.time() 119 | 120 | pred_sbbox, pred_mbbox, pred_lbbox = sess.run( 121 | [return_tensors[1], return_tensors[2], return_tensors[3]], 122 | feed_dict={ return_tensors[0]: image_data}) 123 | 124 | pred_bbox = np.concatenate([np.reshape(pred_sbbox, (-1, 5 + num_classes)), 125 | np.reshape(pred_mbbox, (-1, 5 + num_classes)), 126 | np.reshape(pred_lbbox, (-1, 5 + num_classes))], axis=0) 127 | 128 | bboxes = utils.postprocess_boxes(pred_bbox, frame_size, input_size, 0.3) 129 | bboxes = utils.nms(bboxes, 0.45, method='nms') 130 | image, detected = utils.draw_bbox(frame, bboxes) 131 | 132 | 133 | detected = np.asarray(detected) 134 | 135 | # print("------- frame i ---------") 136 | 137 | class_count = [] 138 | 139 | for i in range(len(obj_classes)): # 80 140 | obj_count = 0 141 | for j in range(len(detected)): 142 | if int(detected[j][5]) == i: obj_count += 1 143 | 144 | class_count = np.append(class_count, obj_count) 145 | 146 | curr_time = time.time() 147 | exec_time = curr_time - prev_time 148 | result = np.asarray(image) 149 | info = "time: %.2f ms" %(1000*exec_time) 150 | # cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE) 151 | result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) 152 | 153 | return result, class_count 154 | # ----------------------------------- 155 | 156 | -------------------------------------------------------------------------------- /static/css/templatemo_style.css: -------------------------------------------------------------------------------- 1 | /* 2 | 3 | templatemo 419 black white 4 | 5 | http://www.templatemo.com/preview/templatemo_419_black_white 6 | 7 | http://stackoverflow.com/questions/10525744/css-table-cell-equal-width 8 | 9 | */ 10 | * { font-family: 'Open Sans', sans-serif; } 11 | 12 | html, body { height: 100%; } 13 | 14 | .green { color: #3B3; } 15 | .blue { color: #09E; } 16 | 17 | h1, h2 { margin-bottom: 20px; } 18 | 19 | h2 { font-size: 24px; } 20 | .templatemo-container { 21 | display: table; 22 | width: 100%; 23 | height: 100%; 24 | table-layout: fixed; 25 | } 26 | .left-container { 27 | display: table-cell; 28 | float: none; 29 | width: 25%; 30 | padding-bottom: 20px; 31 | padding-left: 30px; 32 | padding-right: 0; 33 | } 34 | .right-container { 35 | display: table-cell; 36 | float: none; 37 | width: 75%; 38 | padding-bottom: 20px; 39 | padding-left: 0; 40 | padding-right: 30px; 41 | vertical-align: top; 42 | } 43 | .black-bg { 44 | color: white; 45 | background-color: black; 46 | } 47 | .white-bg { background-color: white; } 48 | .tm-left-inner-container { margin-right: 30px; } 49 | .tm-right-inner-container { 50 | margin-left: 30px; 51 | /*max-width: 710px;*/ 52 | max-width: 1200px; 53 | } 54 | .templatemo-logo { 55 | display: table; 56 | overflow: hidden; 57 | table-layout: fixed; 58 | width: 100% 59 | } 60 | .logo-left-container { 61 | display: table-cell; 62 | width: 38%; 63 | } 64 | .logo-right-container { 65 | display: table-cell; 66 | width: 62%; 67 | } 68 | .templatemo-logo h1 { 69 | margin-top: 0; 70 | margin-bottom: 0; 71 | max-width: 250px; 72 | } 73 | .templatemo-logo .col-lg-6, .templatemo-logo .col-md-6, .templatemo-logo .col-sm-6 { 74 | padding-left: 0; 75 | padding-right: 0; 76 | padding-top: 20px; 77 | } 78 | .logo-left { 79 | border-top: 1px solid gray; 80 | border-right: none; 81 | border-bottom: 1px solid gray; 82 | border-left: 1px solid gray; 83 | float: right; 84 | padding: 20px 30px 20px 100px; 85 | text-align: right; 86 | } 87 | .logo-right { 88 | border-top: 1px solid gray; 89 | border-right: 1px solid gray; 90 | border-bottom: 1px solid gray; 91 | border-left: none; 92 | float: left; 93 | padding: 20px 100px 20px 30px; 94 | } 95 | 96 | .templatemo-nav { 97 | clear: both; 98 | font-size: 16px; 99 | font-weight: 300; 100 | float: right; 101 | min-width: 250px; 102 | } 103 | .templatemo-nav>li>a { 104 | border: 2px solid #8C8C8C; 105 | border-radius: 8px; 106 | color: white; 107 | padding: 15px 50px; 108 | margin-bottom: 20px; 109 | transition: background-color 0.3s; 110 | } 111 | .templatemo-nav>li>a:hover, .templatemo-nav>li>a:focus, .templatemo-nav>li>a.active { 112 | background-color: #fff; 113 | color: #000; 114 | font-weight: 700; 115 | } 116 | .templatemo-nav>li>a>.fa { margin-right: 10px; } 117 | .templatemo-header { 118 | clear: both; 119 | font-size: 30px; 120 | margin-top: 0; 121 | } 122 | article { margin-top: 40px; } 123 | article p { 124 | margin-bottom: 15px; 125 | text-align: justify; 126 | } 127 | footer { 128 | margin-top: 40px; 129 | overflow: hidden; 130 | } 131 | footer p { margin-bottom: 0; } 132 | footer .col-lg-6, footer .col-md-6 { 133 | padding-left: 0; 134 | padding-right: 0; 135 | } 136 | .templatemo-social { text-align: right; } 137 | .templatemo-social i { 138 | border: 1px solid #ccc; 139 | padding-top: 7px; 140 | padding-bottom: 5px; 141 | text-align: center; 142 | width: 30px; 143 | height: 30px; 144 | transition: background-color 0.3s; 145 | } 146 | .templatemo-social a { color: #757575; } 147 | .templatemo-social a:hover i { 148 | background-color: #000; 149 | color: #fff; 150 | } 151 | .row .templatemo-item { margin-top: 0; } 152 | .templatemo-item p { margin-top: 20px; } 153 | .templatemo-item ul { margin-bottom: 20px; } 154 | .templatemo-item ul li { list-style: none; } 155 | .fa.templatemo-service-icon { font-size: 100px; } 156 | 157 | .templatemo-testimonial { overflow: hidden; } 158 | .templatemo-testimonial .fa { 159 | color: #C8B55E; 160 | padding: 5px; 161 | } 162 | .templatemo-border-left { 163 | border-left: 3px solid #C8B55E; 164 | padding-left: 10px; 165 | } 166 | .templatemo-border-right { 167 | border-right: 3px solid #C8B55E; 168 | padding-right: 10px; 169 | text-align: right; 170 | } 171 | .templatemo-testimonial footer { color: #777; } 172 | .templatemo-testimonial footer:before { content: '\2014 \00A0'; } 173 | .form-control:focus { 174 | border-color: #DEB215; 175 | box-shadow: inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(234, 231, 60, 0.6); 176 | } 177 | iframe { border: none; } 178 | #map-canvas { 179 | height: 350px; 180 | margin-bottom: 30px; 181 | } 182 | .margin-bottom-60 { margin-bottom: 60px; } 183 | 184 | /* Media Queries 185 | ----------------------------------------*/ 186 | @media screen and (max-width: 991px) { 187 | .templatemo-social { 188 | margin-top: 15px; 189 | text-align: left; 190 | } 191 | } 192 | @media screen and (max-width: 767px) { 193 | .templatemo-logo, .templatemo-container { display: block; } 194 | .logo-left-container, .logo-right-container, .left-container, .right-container { 195 | display: block; 196 | width: 100%; 197 | } 198 | .left-container, .right-container { 199 | padding-left: 20px; 200 | padding-right: 20px; 201 | } 202 | .logo-left, .logo-right, .templatemo-nav { float: none; } 203 | .logo-left { 204 | border-right: 1px solid gray; 205 | text-align: center; 206 | margin-bottom: 30px; 207 | padding: 20px; 208 | } 209 | .logo-right { 210 | border-left: 1px solid gray; 211 | text-align: center; 212 | margin-bottom: 30px; 213 | padding: 20px; 214 | } 215 | .tm-left-inner-container { margin-right: 0; } 216 | .tm-right-inner-container { margin-left: 0; } 217 | .templatemo-nav { max-width: 100%; } 218 | .templatemo-nav>li>a { 219 | margin-bottom: 5px; 220 | text-align: center; 221 | } 222 | h1 { 223 | margin-top: 0; 224 | margin-bottom: 20px; 225 | } 226 | .templatemo-logo h1 { 227 | border: none; 228 | max-width: none; 229 | } 230 | .left-container, .right-container { padding-top: 40px; } 231 | .templatemo-logo .col-lg-6, .templatemo-logo .col-md-6, .templatemo-logo .col-sm-6 { padding-top: 0; } 232 | } 233 | -------------------------------------------------------------------------------- /webcam/templates/css/templatemo_style.css: -------------------------------------------------------------------------------- 1 | /* 2 | 3 | templatemo 419 black white 4 | 5 | http://www.templatemo.com/preview/templatemo_419_black_white 6 | 7 | http://stackoverflow.com/questions/10525744/css-table-cell-equal-width 8 | 9 | */ 10 | * { font-family: 'Open Sans', sans-serif; } 11 | 12 | html, body { height: 100%; } 13 | 14 | .green { color: #3B3; } 15 | .blue { color: #09E; } 16 | 17 | h1, h2 { margin-bottom: 20px; } 18 | 19 | h2 { font-size: 24px; } 20 | .templatemo-container { 21 | display: table; 22 | width: 100%; 23 | height: 100%; 24 | table-layout: fixed; 25 | } 26 | .left-container { 27 | display: table-cell; 28 | float: none; 29 | width: 25%; 30 | padding-bottom: 20px; 31 | padding-left: 30px; 32 | padding-right: 0; 33 | } 34 | .right-container { 35 | display: table-cell; 36 | float: none; 37 | width: 75%; 38 | padding-bottom: 20px; 39 | padding-left: 0; 40 | padding-right: 30px; 41 | vertical-align: top; 42 | } 43 | .black-bg { 44 | color: white; 45 | background-color: black; 46 | } 47 | .white-bg { background-color: white; } 48 | .tm-left-inner-container { margin-right: 30px; } 49 | .tm-right-inner-container { 50 | margin-left: 30px; 51 | /*max-width: 710px;*/ 52 | max-width: 1200px; 53 | } 54 | .templatemo-logo { 55 | display: table; 56 | overflow: hidden; 57 | table-layout: fixed; 58 | width: 100% 59 | } 60 | .logo-left-container { 61 | display: table-cell; 62 | width: 38%; 63 | } 64 | .logo-right-container { 65 | display: table-cell; 66 | width: 62%; 67 | } 68 | .templatemo-logo h1 { 69 | margin-top: 0; 70 | margin-bottom: 0; 71 | max-width: 250px; 72 | } 73 | .templatemo-logo .col-lg-6, .templatemo-logo .col-md-6, .templatemo-logo .col-sm-6 { 74 | padding-left: 0; 75 | padding-right: 0; 76 | padding-top: 20px; 77 | } 78 | .logo-left { 79 | border-top: 1px solid gray; 80 | border-right: none; 81 | border-bottom: 1px solid gray; 82 | border-left: 1px solid gray; 83 | float: right; 84 | padding: 20px 30px 20px 100px; 85 | text-align: right; 86 | } 87 | .logo-right { 88 | border-top: 1px solid gray; 89 | border-right: 1px solid gray; 90 | border-bottom: 1px solid gray; 91 | border-left: none; 92 | float: left; 93 | padding: 20px 100px 20px 30px; 94 | } 95 | 96 | .templatemo-nav { 97 | clear: both; 98 | font-size: 16px; 99 | font-weight: 300; 100 | float: right; 101 | min-width: 250px; 102 | } 103 | .templatemo-nav>li>a { 104 | border: 2px solid #8C8C8C; 105 | border-radius: 8px; 106 | color: white; 107 | padding: 15px 50px; 108 | margin-bottom: 20px; 109 | transition: background-color 0.3s; 110 | } 111 | .templatemo-nav>li>a:hover, .templatemo-nav>li>a:focus, .templatemo-nav>li>a.active { 112 | background-color: #fff; 113 | color: #000; 114 | font-weight: 700; 115 | } 116 | .templatemo-nav>li>a>.fa { margin-right: 10px; } 117 | .templatemo-header { 118 | clear: both; 119 | font-size: 30px; 120 | margin-top: 0; 121 | } 122 | article { margin-top: 40px; } 123 | article p { 124 | margin-bottom: 15px; 125 | text-align: justify; 126 | } 127 | footer { 128 | margin-top: 40px; 129 | overflow: hidden; 130 | } 131 | footer p { margin-bottom: 0; } 132 | footer .col-lg-6, footer .col-md-6 { 133 | padding-left: 0; 134 | padding-right: 0; 135 | } 136 | .templatemo-social { text-align: right; } 137 | .templatemo-social i { 138 | border: 1px solid #ccc; 139 | padding-top: 7px; 140 | padding-bottom: 5px; 141 | text-align: center; 142 | width: 30px; 143 | height: 30px; 144 | transition: background-color 0.3s; 145 | } 146 | .templatemo-social a { color: #757575; } 147 | .templatemo-social a:hover i { 148 | background-color: #000; 149 | color: #fff; 150 | } 151 | .row .templatemo-item { margin-top: 0; } 152 | .templatemo-item p { margin-top: 20px; } 153 | .templatemo-item ul { margin-bottom: 20px; } 154 | .templatemo-item ul li { list-style: none; } 155 | .fa.templatemo-service-icon { font-size: 100px; } 156 | 157 | .templatemo-testimonial { overflow: hidden; } 158 | .templatemo-testimonial .fa { 159 | color: #C8B55E; 160 | padding: 5px; 161 | } 162 | .templatemo-border-left { 163 | border-left: 3px solid #C8B55E; 164 | padding-left: 10px; 165 | } 166 | .templatemo-border-right { 167 | border-right: 3px solid #C8B55E; 168 | padding-right: 10px; 169 | text-align: right; 170 | } 171 | .templatemo-testimonial footer { color: #777; } 172 | .templatemo-testimonial footer:before { content: '\2014 \00A0'; } 173 | .form-control:focus { 174 | border-color: #DEB215; 175 | box-shadow: inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(234, 231, 60, 0.6); 176 | } 177 | iframe { border: none; } 178 | #map-canvas { 179 | height: 350px; 180 | margin-bottom: 30px; 181 | } 182 | .margin-bottom-60 { margin-bottom: 60px; } 183 | 184 | /* Media Queries 185 | ----------------------------------------*/ 186 | @media screen and (max-width: 991px) { 187 | .templatemo-social { 188 | margin-top: 15px; 189 | text-align: left; 190 | } 191 | } 192 | @media screen and (max-width: 767px) { 193 | .templatemo-logo, .templatemo-container { display: block; } 194 | .logo-left-container, .logo-right-container, .left-container, .right-container { 195 | display: block; 196 | width: 100%; 197 | } 198 | .left-container, .right-container { 199 | padding-left: 20px; 200 | padding-right: 20px; 201 | } 202 | .logo-left, .logo-right, .templatemo-nav { float: none; } 203 | .logo-left { 204 | border-right: 1px solid gray; 205 | text-align: center; 206 | margin-bottom: 30px; 207 | padding: 20px; 208 | } 209 | .logo-right { 210 | border-left: 1px solid gray; 211 | text-align: center; 212 | margin-bottom: 30px; 213 | padding: 20px; 214 | } 215 | .tm-left-inner-container { margin-right: 0; } 216 | .tm-right-inner-container { margin-left: 0; } 217 | .templatemo-nav { max-width: 100%; } 218 | .templatemo-nav>li>a { 219 | margin-bottom: 5px; 220 | text-align: center; 221 | } 222 | h1 { 223 | margin-top: 0; 224 | margin-bottom: 20px; 225 | } 226 | .templatemo-logo h1 { 227 | border: none; 228 | max-width: none; 229 | } 230 | .left-container, .right-container { padding-top: 40px; } 231 | .templatemo-logo .col-lg-6, .templatemo-logo .col-md-6, .templatemo-logo .col-sm-6 { padding-top: 0; } 232 | } 233 | -------------------------------------------------------------------------------- /core/utils.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | # coding=utf-8 3 | #================================================================ 4 | # Copyright (C) 2019 * Ltd. All rights reserved. 5 | # 6 | # Editor : VIM 7 | # File name : utils.py 8 | # Author : YunYang1994 9 | # Created date: 2019-02-28 13:14:19 10 | # Description : 2019-07-23 Modified by Tran Le Anh 11 | # 12 | #================================================================ 13 | 14 | import cv2 15 | import random 16 | import colorsys 17 | import numpy as np 18 | import tensorflow as tf 19 | from core.config import cfg 20 | 21 | def read_class_names(class_file_name): 22 | '''loads class name from a file''' 23 | names = {} 24 | with open(class_file_name, 'r') as data: 25 | for ID, name in enumerate(data): 26 | names[ID] = name.strip('\n') 27 | return names 28 | 29 | def get_anchors(anchors_path): 30 | '''loads the anchors from a file''' 31 | with open(anchors_path) as f: 32 | anchors = f.readline() 33 | anchors = np.array(anchors.split(','), dtype=np.float32) 34 | return anchors.reshape(3, 3, 2) 35 | 36 | 37 | def image_preporcess(image, target_size, gt_boxes=None): 38 | 39 | image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.float32) 40 | 41 | ih, iw = target_size 42 | h, w, _ = image.shape 43 | 44 | scale = min(iw/w, ih/h) 45 | nw, nh = int(scale * w), int(scale * h) 46 | image_resized = cv2.resize(image, (nw, nh)) 47 | 48 | image_paded = np.full(shape=[ih, iw, 3], fill_value=128.0) 49 | dw, dh = (iw - nw) // 2, (ih-nh) // 2 50 | image_paded[dh:nh+dh, dw:nw+dw, :] = image_resized 51 | image_paded = image_paded / 255. 52 | 53 | if gt_boxes is None: 54 | return image_paded 55 | 56 | else: 57 | gt_boxes[:, [0, 2]] = gt_boxes[:, [0, 2]] * scale + dw 58 | gt_boxes[:, [1, 3]] = gt_boxes[:, [1, 3]] * scale + dh 59 | return image_paded, gt_boxes 60 | 61 | 62 | def draw_bbox(image, bboxes, classes=read_class_names(cfg.YOLO.CLASSES), show_label=True): 63 | """ 64 | bboxes: [x_min, y_min, x_max, y_max, probability, cls_id] format coordinates. 65 | """ 66 | data = open(cfg.YOLO.CLASSES, 'r') 67 | 68 | num_classes = len(classes) 69 | image_h, image_w, _ = image.shape 70 | hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)] 71 | colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples)) 72 | colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors)) 73 | 74 | random.seed(0) 75 | random.shuffle(colors) 76 | random.seed(None) 77 | 78 | for i, bbox in enumerate(bboxes): 79 | coor = np.array(bbox[:4], dtype=np.int32) 80 | fontScale = 0.5 81 | score = bbox[4] 82 | class_ind = int(bbox[5]) 83 | bbox_color = colors[class_ind] 84 | bbox_thick = int(0.6 * (image_h + image_w) / 600) 85 | c1, c2 = (coor[0], coor[1]), (coor[2], coor[3]) 86 | cv2.rectangle(image, c1, c2, bbox_color, bbox_thick) 87 | 88 | if show_label: 89 | bbox_mess = '%s: %.2f' % (classes[class_ind], score) 90 | t_size = cv2.getTextSize(bbox_mess, 0, fontScale, thickness=bbox_thick//2)[0] 91 | cv2.rectangle(image, c1, (c1[0] + t_size[0], c1[1] - t_size[1] - 3), bbox_color, -1) # filled 92 | 93 | cv2.putText(image, bbox_mess, (c1[0], c1[1]-2), cv2.FONT_HERSHEY_SIMPLEX, 94 | fontScale, (0, 0, 0), bbox_thick//2, lineType=cv2.LINE_AA) 95 | 96 | return image, bboxes 97 | 98 | 99 | 100 | def bboxes_iou(boxes1, boxes2): 101 | 102 | boxes1 = np.array(boxes1) 103 | boxes2 = np.array(boxes2) 104 | 105 | boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1]) 106 | boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1]) 107 | 108 | left_up = np.maximum(boxes1[..., :2], boxes2[..., :2]) 109 | right_down = np.minimum(boxes1[..., 2:], boxes2[..., 2:]) 110 | 111 | inter_section = np.maximum(right_down - left_up, 0.0) 112 | inter_area = inter_section[..., 0] * inter_section[..., 1] 113 | union_area = boxes1_area + boxes2_area - inter_area 114 | ious = np.maximum(1.0 * inter_area / union_area, np.finfo(np.float32).eps) 115 | 116 | return ious 117 | 118 | 119 | 120 | def read_pb_return_tensors(graph, pb_file, return_elements): 121 | 122 | # with tf.gfile.FastGFile(pb_file, 'rb') as f: 123 | with tf.io.gfile.GFile(pb_file, 'rb') as f: 124 | # frozen_graph_def = tf.GraphDef() 125 | frozen_graph_def = tf.compat.v1.GraphDef() 126 | frozen_graph_def.ParseFromString(f.read()) 127 | 128 | with graph.as_default(): 129 | return_elements = tf.import_graph_def(frozen_graph_def, 130 | return_elements=return_elements) 131 | return return_elements 132 | 133 | 134 | def nms(bboxes, iou_threshold, sigma=0.3, method='nms'): 135 | """ 136 | :param bboxes: (xmin, ymin, xmax, ymax, score, class) 137 | 138 | Note: soft-nms, https://arxiv.org/pdf/1704.04503.pdf 139 | https://github.com/bharatsingh430/soft-nms 140 | """ 141 | classes_in_img = list(set(bboxes[:, 5])) 142 | best_bboxes = [] 143 | 144 | for cls in classes_in_img: 145 | cls_mask = (bboxes[:, 5] == cls) 146 | cls_bboxes = bboxes[cls_mask] 147 | 148 | while len(cls_bboxes) > 0: 149 | max_ind = np.argmax(cls_bboxes[:, 4]) 150 | best_bbox = cls_bboxes[max_ind] 151 | best_bboxes.append(best_bbox) 152 | cls_bboxes = np.concatenate([cls_bboxes[: max_ind], cls_bboxes[max_ind + 1:]]) 153 | iou = bboxes_iou(best_bbox[np.newaxis, :4], cls_bboxes[:, :4]) 154 | weight = np.ones((len(iou),), dtype=np.float32) 155 | 156 | assert method in ['nms', 'soft-nms'] 157 | 158 | if method == 'nms': 159 | iou_mask = iou > iou_threshold 160 | weight[iou_mask] = 0.0 161 | 162 | if method == 'soft-nms': 163 | weight = np.exp(-(1.0 * iou ** 2 / sigma)) 164 | 165 | cls_bboxes[:, 4] = cls_bboxes[:, 4] * weight 166 | score_mask = cls_bboxes[:, 4] > 0. 167 | cls_bboxes = cls_bboxes[score_mask] 168 | 169 | return best_bboxes 170 | 171 | 172 | def postprocess_boxes(pred_bbox, org_img_shape, input_size, score_threshold): 173 | 174 | valid_scale=[0, np.inf] 175 | pred_bbox = np.array(pred_bbox) 176 | 177 | pred_xywh = pred_bbox[:, 0:4] 178 | pred_conf = pred_bbox[:, 4] 179 | pred_prob = pred_bbox[:, 5:] 180 | 181 | # # (1) (x, y, w, h) --> (xmin, ymin, xmax, ymax) 182 | pred_coor = np.concatenate([pred_xywh[:, :2] - pred_xywh[:, 2:] * 0.5, 183 | pred_xywh[:, :2] + pred_xywh[:, 2:] * 0.5], axis=-1) 184 | # # (2) (xmin, ymin, xmax, ymax) -> (xmin_org, ymin_org, xmax_org, ymax_org) 185 | org_h, org_w = org_img_shape 186 | resize_ratio = min(input_size / org_w, input_size / org_h) 187 | 188 | dw = (input_size - resize_ratio * org_w) / 2 189 | dh = (input_size - resize_ratio * org_h) / 2 190 | 191 | pred_coor[:, 0::2] = 1.0 * (pred_coor[:, 0::2] - dw) / resize_ratio 192 | pred_coor[:, 1::2] = 1.0 * (pred_coor[:, 1::2] - dh) / resize_ratio 193 | 194 | # # (3) clip some boxes those are out of range 195 | pred_coor = np.concatenate([np.maximum(pred_coor[:, :2], [0, 0]), 196 | np.minimum(pred_coor[:, 2:], [org_w - 1, org_h - 1])], axis=-1) 197 | invalid_mask = np.logical_or((pred_coor[:, 0] > pred_coor[:, 2]), (pred_coor[:, 1] > pred_coor[:, 3])) 198 | pred_coor[invalid_mask] = 0 199 | 200 | # # (4) discard some invalid boxes 201 | bboxes_scale = np.sqrt(np.multiply.reduce(pred_coor[:, 2:4] - pred_coor[:, 0:2], axis=-1)) 202 | scale_mask = np.logical_and((valid_scale[0] < bboxes_scale), (bboxes_scale < valid_scale[1])) 203 | 204 | # # (5) discard some boxes with low scores 205 | classes = np.argmax(pred_prob, axis=-1) 206 | scores = pred_conf * pred_prob[np.arange(len(pred_coor)), classes] 207 | score_mask = scores > score_threshold 208 | mask = np.logical_and(scale_mask, score_mask) 209 | coors, scores, classes = pred_coor[mask], scores[mask], classes[mask] 210 | 211 | return np.concatenate([coors, scores[:, np.newaxis], classes[:, np.newaxis]], axis=-1) 212 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | # coding=utf-8 3 | #================================================================ 4 | # Copyright (C) 2019 * Ltd. All rights reserved. 5 | # 6 | # Editor : VIM 7 | # File name : train.py 8 | # Author : YunYang1994 9 | # Created date: 2019-02-28 17:50:26 10 | # Description : 11 | # 12 | #================================================================ 13 | 14 | import os 15 | import time 16 | import shutil 17 | import numpy as np 18 | import tensorflow as tf 19 | import core.utils as utils 20 | from tqdm import tqdm 21 | from core.dataset import Dataset 22 | from core.yolov3 import YOLOV3 23 | from core.config import cfg 24 | 25 | 26 | class YoloTrain(object): 27 | def __init__(self): 28 | self.anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE 29 | self.classes = utils.read_class_names(cfg.YOLO.CLASSES) 30 | self.num_classes = len(self.classes) 31 | self.learn_rate_init = cfg.TRAIN.LEARN_RATE_INIT 32 | self.learn_rate_end = cfg.TRAIN.LEARN_RATE_END 33 | self.first_stage_epochs = cfg.TRAIN.FISRT_STAGE_EPOCHS 34 | self.second_stage_epochs = cfg.TRAIN.SECOND_STAGE_EPOCHS 35 | self.warmup_periods = cfg.TRAIN.WARMUP_EPOCHS 36 | self.initial_weight = cfg.TRAIN.INITIAL_WEIGHT 37 | self.time = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time())) 38 | self.moving_ave_decay = cfg.YOLO.MOVING_AVE_DECAY 39 | self.max_bbox_per_scale = 150 40 | self.train_logdir = "./data/log/train" 41 | self.trainset = Dataset('train') 42 | self.testset = Dataset('test') 43 | self.steps_per_period = len(self.trainset) 44 | self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) 45 | 46 | with tf.name_scope('define_input'): 47 | self.input_data = tf.placeholder(dtype=tf.float32, name='input_data') 48 | self.label_sbbox = tf.placeholder(dtype=tf.float32, name='label_sbbox') 49 | self.label_mbbox = tf.placeholder(dtype=tf.float32, name='label_mbbox') 50 | self.label_lbbox = tf.placeholder(dtype=tf.float32, name='label_lbbox') 51 | self.true_sbboxes = tf.placeholder(dtype=tf.float32, name='sbboxes') 52 | self.true_mbboxes = tf.placeholder(dtype=tf.float32, name='mbboxes') 53 | self.true_lbboxes = tf.placeholder(dtype=tf.float32, name='lbboxes') 54 | self.trainable = tf.placeholder(dtype=tf.bool, name='training') 55 | 56 | with tf.name_scope("define_loss"): 57 | self.model = YOLOV3(self.input_data, self.trainable) 58 | self.net_var = tf.global_variables() 59 | self.giou_loss, self.conf_loss, self.prob_loss = self.model.compute_loss( 60 | self.label_sbbox, self.label_mbbox, self.label_lbbox, 61 | self.true_sbboxes, self.true_mbboxes, self.true_lbboxes) 62 | self.loss = self.giou_loss + self.conf_loss + self.prob_loss 63 | 64 | with tf.name_scope('learn_rate'): 65 | self.global_step = tf.Variable(1.0, dtype=tf.float64, trainable=False, name='global_step') 66 | warmup_steps = tf.constant(self.warmup_periods * self.steps_per_period, 67 | dtype=tf.float64, name='warmup_steps') 68 | train_steps = tf.constant( (self.first_stage_epochs + self.second_stage_epochs)* self.steps_per_period, 69 | dtype=tf.float64, name='train_steps') 70 | self.learn_rate = tf.cond( 71 | pred=self.global_step < warmup_steps, 72 | true_fn=lambda: self.global_step / warmup_steps * self.learn_rate_init, 73 | false_fn=lambda: self.learn_rate_end + 0.5 * (self.learn_rate_init - self.learn_rate_end) * 74 | (1 + tf.cos( 75 | (self.global_step - warmup_steps) / (train_steps - warmup_steps) * np.pi)) 76 | ) 77 | global_step_update = tf.assign_add(self.global_step, 1.0) 78 | 79 | with tf.name_scope("define_weight_decay"): 80 | moving_ave = tf.train.ExponentialMovingAverage(self.moving_ave_decay).apply(tf.trainable_variables()) 81 | 82 | with tf.name_scope("define_first_stage_train"): 83 | self.first_stage_trainable_var_list = [] 84 | for var in tf.trainable_variables(): 85 | var_name = var.op.name 86 | var_name_mess = str(var_name).split('/') 87 | if var_name_mess[0] in ['conv_sbbox', 'conv_mbbox', 'conv_lbbox']: 88 | self.first_stage_trainable_var_list.append(var) 89 | 90 | first_stage_optimizer = tf.train.AdamOptimizer(self.learn_rate).minimize(self.loss, 91 | var_list=self.first_stage_trainable_var_list) 92 | with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)): 93 | with tf.control_dependencies([first_stage_optimizer, global_step_update]): 94 | with tf.control_dependencies([moving_ave]): 95 | self.train_op_with_frozen_variables = tf.no_op() 96 | 97 | with tf.name_scope("define_second_stage_train"): 98 | second_stage_trainable_var_list = tf.trainable_variables() 99 | second_stage_optimizer = tf.train.AdamOptimizer(self.learn_rate).minimize(self.loss, 100 | var_list=second_stage_trainable_var_list) 101 | 102 | with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)): 103 | with tf.control_dependencies([second_stage_optimizer, global_step_update]): 104 | with tf.control_dependencies([moving_ave]): 105 | self.train_op_with_all_variables = tf.no_op() 106 | 107 | with tf.name_scope('loader_and_saver'): 108 | self.loader = tf.train.Saver(self.net_var) 109 | self.saver = tf.train.Saver(tf.global_variables(), max_to_keep=10) 110 | 111 | with tf.name_scope('summary'): 112 | tf.summary.scalar("learn_rate", self.learn_rate) 113 | tf.summary.scalar("giou_loss", self.giou_loss) 114 | tf.summary.scalar("conf_loss", self.conf_loss) 115 | tf.summary.scalar("prob_loss", self.prob_loss) 116 | tf.summary.scalar("total_loss", self.loss) 117 | 118 | logdir = "./data/log/" 119 | if os.path.exists(logdir): shutil.rmtree(logdir) 120 | os.mkdir(logdir) 121 | self.write_op = tf.summary.merge_all() 122 | self.summary_writer = tf.summary.FileWriter(logdir, graph=self.sess.graph) 123 | 124 | 125 | def train(self): 126 | self.sess.run(tf.global_variables_initializer()) 127 | try: 128 | print('=> Restoring weights from: %s ... ' % self.initial_weight) 129 | self.loader.restore(self.sess, self.initial_weight) 130 | except: 131 | print('=> %s does not exist !!!' % self.initial_weight) 132 | print('=> Now it starts to train YOLOV3 from scratch ...') 133 | self.first_stage_epochs = 0 134 | 135 | for epoch in range(1, 1+self.first_stage_epochs+self.second_stage_epochs): 136 | if epoch <= self.first_stage_epochs: 137 | train_op = self.train_op_with_frozen_variables 138 | else: 139 | train_op = self.train_op_with_all_variables 140 | 141 | pbar = tqdm(self.trainset) 142 | train_epoch_loss, test_epoch_loss = [], [] 143 | 144 | for train_data in pbar: 145 | _, summary, train_step_loss, global_step_val = self.sess.run( 146 | [train_op, self.write_op, self.loss, self.global_step],feed_dict={ 147 | self.input_data: train_data[0], 148 | self.label_sbbox: train_data[1], 149 | self.label_mbbox: train_data[2], 150 | self.label_lbbox: train_data[3], 151 | self.true_sbboxes: train_data[4], 152 | self.true_mbboxes: train_data[5], 153 | self.true_lbboxes: train_data[6], 154 | self.trainable: True, 155 | }) 156 | 157 | train_epoch_loss.append(train_step_loss) 158 | self.summary_writer.add_summary(summary, global_step_val) 159 | pbar.set_description("train loss: %.2f" %train_step_loss) 160 | 161 | for test_data in self.testset: 162 | test_step_loss = self.sess.run( self.loss, feed_dict={ 163 | self.input_data: test_data[0], 164 | self.label_sbbox: test_data[1], 165 | self.label_mbbox: test_data[2], 166 | self.label_lbbox: test_data[3], 167 | self.true_sbboxes: test_data[4], 168 | self.true_mbboxes: test_data[5], 169 | self.true_lbboxes: test_data[6], 170 | self.trainable: False, 171 | }) 172 | 173 | test_epoch_loss.append(test_step_loss) 174 | 175 | train_epoch_loss, test_epoch_loss = np.mean(train_epoch_loss), np.mean(test_epoch_loss) 176 | ckpt_file = "./checkpoint/yolov3_test_loss=%.4f.ckpt" % test_epoch_loss 177 | log_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) 178 | print("=> Epoch: %2d Time: %s Train loss: %.2f Test loss: %.2f Saving %s ..." 179 | %(epoch, log_time, train_epoch_loss, test_epoch_loss, ckpt_file)) 180 | self.saver.save(self.sess, ckpt_file, global_step=epoch) 181 | 182 | 183 | 184 | if __name__ == '__main__': YoloTrain().train() 185 | 186 | 187 | 188 | 189 | -------------------------------------------------------------------------------- /core/dataset.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | # coding=utf-8 3 | #================================================================ 4 | # Copyright (C) 2019 * Ltd. All rights reserved. 5 | # 6 | # Editor : VIM 7 | # File name : dataset.py 8 | # Author : YunYang1994 9 | # Created date: 2019-03-15 18:05:03 10 | # Description : 11 | # 12 | #================================================================ 13 | 14 | import os 15 | import cv2 16 | import random 17 | import numpy as np 18 | import tensorflow as tf 19 | import core.utils as utils 20 | from core.config import cfg 21 | 22 | 23 | 24 | class Dataset(object): 25 | """implement Dataset here""" 26 | def __init__(self, dataset_type): 27 | self.annot_path = cfg.TRAIN.ANNOT_PATH if dataset_type == 'train' else cfg.TEST.ANNOT_PATH 28 | self.input_sizes = cfg.TRAIN.INPUT_SIZE if dataset_type == 'train' else cfg.TEST.INPUT_SIZE 29 | self.batch_size = cfg.TRAIN.BATCH_SIZE if dataset_type == 'train' else cfg.TEST.BATCH_SIZE 30 | self.data_aug = cfg.TRAIN.DATA_AUG if dataset_type == 'train' else cfg.TEST.DATA_AUG 31 | 32 | self.train_input_sizes = cfg.TRAIN.INPUT_SIZE 33 | self.strides = np.array(cfg.YOLO.STRIDES) 34 | self.classes = utils.read_class_names(cfg.YOLO.CLASSES) 35 | self.num_classes = len(self.classes) 36 | self.anchors = np.array(utils.get_anchors(cfg.YOLO.ANCHORS)) 37 | self.anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE 38 | self.max_bbox_per_scale = 150 39 | 40 | self.annotations = self.load_annotations(dataset_type) 41 | self.num_samples = len(self.annotations) 42 | self.num_batchs = int(np.ceil(self.num_samples / self.batch_size)) 43 | self.batch_count = 0 44 | 45 | 46 | def load_annotations(self, dataset_type): 47 | with open(self.annot_path, 'r') as f: 48 | txt = f.readlines() 49 | annotations = [line.strip() for line in txt if len(line.strip().split()[1:]) != 0] 50 | np.random.shuffle(annotations) 51 | return annotations 52 | 53 | def __iter__(self): 54 | return self 55 | 56 | def __next__(self): 57 | 58 | with tf.device('/cpu:0'): 59 | self.train_input_size = random.choice(self.train_input_sizes) 60 | self.train_output_sizes = self.train_input_size // self.strides 61 | 62 | batch_image = np.zeros((self.batch_size, self.train_input_size, self.train_input_size, 3)) 63 | 64 | batch_label_sbbox = np.zeros((self.batch_size, self.train_output_sizes[0], self.train_output_sizes[0], 65 | self.anchor_per_scale, 5 + self.num_classes)) 66 | batch_label_mbbox = np.zeros((self.batch_size, self.train_output_sizes[1], self.train_output_sizes[1], 67 | self.anchor_per_scale, 5 + self.num_classes)) 68 | batch_label_lbbox = np.zeros((self.batch_size, self.train_output_sizes[2], self.train_output_sizes[2], 69 | self.anchor_per_scale, 5 + self.num_classes)) 70 | 71 | batch_sbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4)) 72 | batch_mbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4)) 73 | batch_lbboxes = np.zeros((self.batch_size, self.max_bbox_per_scale, 4)) 74 | 75 | num = 0 76 | if self.batch_count < self.num_batchs: 77 | while num < self.batch_size: 78 | index = self.batch_count * self.batch_size + num 79 | if index >= self.num_samples: index -= self.num_samples 80 | annotation = self.annotations[index] 81 | image, bboxes = self.parse_annotation(annotation) 82 | label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes = self.preprocess_true_boxes(bboxes) 83 | 84 | batch_image[num, :, :, :] = image 85 | batch_label_sbbox[num, :, :, :, :] = label_sbbox 86 | batch_label_mbbox[num, :, :, :, :] = label_mbbox 87 | batch_label_lbbox[num, :, :, :, :] = label_lbbox 88 | batch_sbboxes[num, :, :] = sbboxes 89 | batch_mbboxes[num, :, :] = mbboxes 90 | batch_lbboxes[num, :, :] = lbboxes 91 | num += 1 92 | self.batch_count += 1 93 | return batch_image, batch_label_sbbox, batch_label_mbbox, batch_label_lbbox, \ 94 | batch_sbboxes, batch_mbboxes, batch_lbboxes 95 | else: 96 | self.batch_count = 0 97 | np.random.shuffle(self.annotations) 98 | raise StopIteration 99 | 100 | def random_horizontal_flip(self, image, bboxes): 101 | 102 | if random.random() < 0.5: 103 | _, w, _ = image.shape 104 | image = image[:, ::-1, :] 105 | bboxes[:, [0,2]] = w - bboxes[:, [2,0]] 106 | 107 | return image, bboxes 108 | 109 | def random_crop(self, image, bboxes): 110 | 111 | if random.random() < 0.5: 112 | h, w, _ = image.shape 113 | max_bbox = np.concatenate([np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0)], axis=-1) 114 | 115 | max_l_trans = max_bbox[0] 116 | max_u_trans = max_bbox[1] 117 | max_r_trans = w - max_bbox[2] 118 | max_d_trans = h - max_bbox[3] 119 | 120 | crop_xmin = max(0, int(max_bbox[0] - random.uniform(0, max_l_trans))) 121 | crop_ymin = max(0, int(max_bbox[1] - random.uniform(0, max_u_trans))) 122 | crop_xmax = max(w, int(max_bbox[2] + random.uniform(0, max_r_trans))) 123 | crop_ymax = max(h, int(max_bbox[3] + random.uniform(0, max_d_trans))) 124 | 125 | image = image[crop_ymin : crop_ymax, crop_xmin : crop_xmax] 126 | 127 | bboxes[:, [0, 2]] = bboxes[:, [0, 2]] - crop_xmin 128 | bboxes[:, [1, 3]] = bboxes[:, [1, 3]] - crop_ymin 129 | 130 | return image, bboxes 131 | 132 | def random_translate(self, image, bboxes): 133 | 134 | if random.random() < 0.5: 135 | h, w, _ = image.shape 136 | max_bbox = np.concatenate([np.min(bboxes[:, 0:2], axis=0), np.max(bboxes[:, 2:4], axis=0)], axis=-1) 137 | 138 | max_l_trans = max_bbox[0] 139 | max_u_trans = max_bbox[1] 140 | max_r_trans = w - max_bbox[2] 141 | max_d_trans = h - max_bbox[3] 142 | 143 | tx = random.uniform(-(max_l_trans - 1), (max_r_trans - 1)) 144 | ty = random.uniform(-(max_u_trans - 1), (max_d_trans - 1)) 145 | 146 | M = np.array([[1, 0, tx], [0, 1, ty]]) 147 | image = cv2.warpAffine(image, M, (w, h)) 148 | 149 | bboxes[:, [0, 2]] = bboxes[:, [0, 2]] + tx 150 | bboxes[:, [1, 3]] = bboxes[:, [1, 3]] + ty 151 | 152 | return image, bboxes 153 | 154 | def parse_annotation(self, annotation): 155 | 156 | line = annotation.split() 157 | image_path = line[0] 158 | if not os.path.exists(image_path): 159 | raise KeyError("%s does not exist ... " %image_path) 160 | image = np.array(cv2.imread(image_path)) 161 | bboxes = np.array([list(map(int, box.split(','))) for box in line[1:]]) 162 | 163 | if self.data_aug: 164 | image, bboxes = self.random_horizontal_flip(np.copy(image), np.copy(bboxes)) 165 | image, bboxes = self.random_crop(np.copy(image), np.copy(bboxes)) 166 | image, bboxes = self.random_translate(np.copy(image), np.copy(bboxes)) 167 | 168 | image, bboxes = utils.image_preporcess(np.copy(image), [self.train_input_size, self.train_input_size], np.copy(bboxes)) 169 | return image, bboxes 170 | 171 | def bbox_iou(self, boxes1, boxes2): 172 | 173 | boxes1 = np.array(boxes1) 174 | boxes2 = np.array(boxes2) 175 | 176 | boxes1_area = boxes1[..., 2] * boxes1[..., 3] 177 | boxes2_area = boxes2[..., 2] * boxes2[..., 3] 178 | 179 | boxes1 = np.concatenate([boxes1[..., :2] - boxes1[..., 2:] * 0.5, 180 | boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1) 181 | boxes2 = np.concatenate([boxes2[..., :2] - boxes2[..., 2:] * 0.5, 182 | boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1) 183 | 184 | left_up = np.maximum(boxes1[..., :2], boxes2[..., :2]) 185 | right_down = np.minimum(boxes1[..., 2:], boxes2[..., 2:]) 186 | 187 | inter_section = np.maximum(right_down - left_up, 0.0) 188 | inter_area = inter_section[..., 0] * inter_section[..., 1] 189 | union_area = boxes1_area + boxes2_area - inter_area 190 | 191 | return inter_area / union_area 192 | 193 | def preprocess_true_boxes(self, bboxes): 194 | 195 | label = [np.zeros((self.train_output_sizes[i], self.train_output_sizes[i], self.anchor_per_scale, 196 | 5 + self.num_classes)) for i in range(3)] 197 | bboxes_xywh = [np.zeros((self.max_bbox_per_scale, 4)) for _ in range(3)] 198 | bbox_count = np.zeros((3,)) 199 | 200 | for bbox in bboxes: 201 | bbox_coor = bbox[:4] 202 | bbox_class_ind = bbox[4] 203 | 204 | onehot = np.zeros(self.num_classes, dtype=np.float) 205 | onehot[bbox_class_ind] = 1.0 206 | uniform_distribution = np.full(self.num_classes, 1.0 / self.num_classes) 207 | deta = 0.01 208 | smooth_onehot = onehot * (1 - deta) + deta * uniform_distribution 209 | 210 | bbox_xywh = np.concatenate([(bbox_coor[2:] + bbox_coor[:2]) * 0.5, bbox_coor[2:] - bbox_coor[:2]], axis=-1) 211 | bbox_xywh_scaled = 1.0 * bbox_xywh[np.newaxis, :] / self.strides[:, np.newaxis] 212 | 213 | iou = [] 214 | exist_positive = False 215 | for i in range(3): 216 | anchors_xywh = np.zeros((self.anchor_per_scale, 4)) 217 | anchors_xywh[:, 0:2] = np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32) + 0.5 218 | anchors_xywh[:, 2:4] = self.anchors[i] 219 | 220 | iou_scale = self.bbox_iou(bbox_xywh_scaled[i][np.newaxis, :], anchors_xywh) 221 | iou.append(iou_scale) 222 | iou_mask = iou_scale > 0.3 223 | 224 | if np.any(iou_mask): 225 | xind, yind = np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32) 226 | 227 | label[i][yind, xind, iou_mask, :] = 0 228 | label[i][yind, xind, iou_mask, 0:4] = bbox_xywh 229 | label[i][yind, xind, iou_mask, 4:5] = 1.0 230 | label[i][yind, xind, iou_mask, 5:] = smooth_onehot 231 | 232 | bbox_ind = int(bbox_count[i] % self.max_bbox_per_scale) 233 | bboxes_xywh[i][bbox_ind, :4] = bbox_xywh 234 | bbox_count[i] += 1 235 | 236 | exist_positive = True 237 | 238 | if not exist_positive: 239 | best_anchor_ind = np.argmax(np.array(iou).reshape(-1), axis=-1) 240 | best_detect = int(best_anchor_ind / self.anchor_per_scale) 241 | best_anchor = int(best_anchor_ind % self.anchor_per_scale) 242 | xind, yind = np.floor(bbox_xywh_scaled[best_detect, 0:2]).astype(np.int32) 243 | 244 | label[best_detect][yind, xind, best_anchor, :] = 0 245 | label[best_detect][yind, xind, best_anchor, 0:4] = bbox_xywh 246 | label[best_detect][yind, xind, best_anchor, 4:5] = 1.0 247 | label[best_detect][yind, xind, best_anchor, 5:] = smooth_onehot 248 | 249 | bbox_ind = int(bbox_count[best_detect] % self.max_bbox_per_scale) 250 | bboxes_xywh[best_detect][bbox_ind, :4] = bbox_xywh 251 | bbox_count[best_detect] += 1 252 | label_sbbox, label_mbbox, label_lbbox = label 253 | sbboxes, mbboxes, lbboxes = bboxes_xywh 254 | return label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes 255 | 256 | def __len__(self): 257 | return self.num_batchs 258 | 259 | 260 | 261 | 262 | -------------------------------------------------------------------------------- /core/yolov3.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | # coding=utf-8 3 | #================================================================ 4 | # Copyright (C) 2019 * Ltd. All rights reserved. 5 | # 6 | # Editor : VIM 7 | # File name : yolov3.py 8 | # Author : YunYang1994 9 | # Created date: 2019-02-28 10:47:03 10 | # Description : 11 | # 12 | #================================================================ 13 | 14 | import numpy as np 15 | import tensorflow as tf 16 | import core.utils as utils 17 | import core.common as common 18 | import core.backbone as backbone 19 | from core.config import cfg 20 | 21 | 22 | class YOLOV3(object): 23 | """Implement tensoflow yolov3 here""" 24 | def __init__(self, input_data, trainable): 25 | 26 | self.trainable = trainable 27 | self.classes = utils.read_class_names(cfg.YOLO.CLASSES) 28 | self.num_class = len(self.classes) 29 | self.strides = np.array(cfg.YOLO.STRIDES) 30 | self.anchors = utils.get_anchors(cfg.YOLO.ANCHORS) 31 | self.anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE 32 | self.iou_loss_thresh = cfg.YOLO.IOU_LOSS_THRESH 33 | self.upsample_method = cfg.YOLO.UPSAMPLE_METHOD 34 | 35 | try: 36 | self.conv_lbbox, self.conv_mbbox, self.conv_sbbox = self.__build_nework(input_data) 37 | except: 38 | raise NotImplementedError("Can not build up yolov3 network!") 39 | 40 | with tf.variable_scope('pred_sbbox'): 41 | self.pred_sbbox = self.decode(self.conv_sbbox, self.anchors[0], self.strides[0]) 42 | 43 | with tf.variable_scope('pred_mbbox'): 44 | self.pred_mbbox = self.decode(self.conv_mbbox, self.anchors[1], self.strides[1]) 45 | 46 | with tf.variable_scope('pred_lbbox'): 47 | self.pred_lbbox = self.decode(self.conv_lbbox, self.anchors[2], self.strides[2]) 48 | 49 | def __build_nework(self, input_data): 50 | 51 | route_1, route_2, input_data = backbone.darknet53(input_data, self.trainable) 52 | 53 | input_data = common.convolutional(input_data, (1, 1, 1024, 512), self.trainable, 'conv52') 54 | input_data = common.convolutional(input_data, (3, 3, 512, 1024), self.trainable, 'conv53') 55 | input_data = common.convolutional(input_data, (1, 1, 1024, 512), self.trainable, 'conv54') 56 | input_data = common.convolutional(input_data, (3, 3, 512, 1024), self.trainable, 'conv55') 57 | input_data = common.convolutional(input_data, (1, 1, 1024, 512), self.trainable, 'conv56') 58 | 59 | conv_lobj_branch = common.convolutional(input_data, (3, 3, 512, 1024), self.trainable, name='conv_lobj_branch') 60 | conv_lbbox = common.convolutional(conv_lobj_branch, (1, 1, 1024, 3*(self.num_class + 5)), 61 | trainable=self.trainable, name='conv_lbbox', activate=False, bn=False) 62 | 63 | input_data = common.convolutional(input_data, (1, 1, 512, 256), self.trainable, 'conv57') 64 | input_data = common.upsample(input_data, name='upsample0', method=self.upsample_method) 65 | 66 | with tf.variable_scope('route_1'): 67 | input_data = tf.concat([input_data, route_2], axis=-1) 68 | 69 | input_data = common.convolutional(input_data, (1, 1, 768, 256), self.trainable, 'conv58') 70 | input_data = common.convolutional(input_data, (3, 3, 256, 512), self.trainable, 'conv59') 71 | input_data = common.convolutional(input_data, (1, 1, 512, 256), self.trainable, 'conv60') 72 | input_data = common.convolutional(input_data, (3, 3, 256, 512), self.trainable, 'conv61') 73 | input_data = common.convolutional(input_data, (1, 1, 512, 256), self.trainable, 'conv62') 74 | 75 | conv_mobj_branch = common.convolutional(input_data, (3, 3, 256, 512), self.trainable, name='conv_mobj_branch' ) 76 | conv_mbbox = common.convolutional(conv_mobj_branch, (1, 1, 512, 3*(self.num_class + 5)), 77 | trainable=self.trainable, name='conv_mbbox', activate=False, bn=False) 78 | 79 | input_data = common.convolutional(input_data, (1, 1, 256, 128), self.trainable, 'conv63') 80 | input_data = common.upsample(input_data, name='upsample1', method=self.upsample_method) 81 | 82 | with tf.variable_scope('route_2'): 83 | input_data = tf.concat([input_data, route_1], axis=-1) 84 | 85 | input_data = common.convolutional(input_data, (1, 1, 384, 128), self.trainable, 'conv64') 86 | input_data = common.convolutional(input_data, (3, 3, 128, 256), self.trainable, 'conv65') 87 | input_data = common.convolutional(input_data, (1, 1, 256, 128), self.trainable, 'conv66') 88 | input_data = common.convolutional(input_data, (3, 3, 128, 256), self.trainable, 'conv67') 89 | input_data = common.convolutional(input_data, (1, 1, 256, 128), self.trainable, 'conv68') 90 | 91 | conv_sobj_branch = common.convolutional(input_data, (3, 3, 128, 256), self.trainable, name='conv_sobj_branch') 92 | conv_sbbox = common.convolutional(conv_sobj_branch, (1, 1, 256, 3*(self.num_class + 5)), 93 | trainable=self.trainable, name='conv_sbbox', activate=False, bn=False) 94 | 95 | return conv_lbbox, conv_mbbox, conv_sbbox 96 | 97 | def decode(self, conv_output, anchors, stride): 98 | """ 99 | return tensor of shape [batch_size, output_size, output_size, anchor_per_scale, 5 + num_classes] 100 | contains (x, y, w, h, score, probability) 101 | """ 102 | 103 | conv_shape = tf.shape(conv_output) 104 | batch_size = conv_shape[0] 105 | output_size = conv_shape[1] 106 | anchor_per_scale = len(anchors) 107 | 108 | conv_output = tf.reshape(conv_output, (batch_size, output_size, output_size, anchor_per_scale, 5 + self.num_class)) 109 | 110 | conv_raw_dxdy = conv_output[:, :, :, :, 0:2] 111 | conv_raw_dwdh = conv_output[:, :, :, :, 2:4] 112 | conv_raw_conf = conv_output[:, :, :, :, 4:5] 113 | conv_raw_prob = conv_output[:, :, :, :, 5: ] 114 | 115 | y = tf.tile(tf.range(output_size, dtype=tf.int32)[:, tf.newaxis], [1, output_size]) 116 | x = tf.tile(tf.range(output_size, dtype=tf.int32)[tf.newaxis, :], [output_size, 1]) 117 | 118 | xy_grid = tf.concat([x[:, :, tf.newaxis], y[:, :, tf.newaxis]], axis=-1) 119 | xy_grid = tf.tile(xy_grid[tf.newaxis, :, :, tf.newaxis, :], [batch_size, 1, 1, anchor_per_scale, 1]) 120 | xy_grid = tf.cast(xy_grid, tf.float32) 121 | 122 | pred_xy = (tf.sigmoid(conv_raw_dxdy) + xy_grid) * stride 123 | pred_wh = (tf.exp(conv_raw_dwdh) * anchors) * stride 124 | pred_xywh = tf.concat([pred_xy, pred_wh], axis=-1) 125 | 126 | pred_conf = tf.sigmoid(conv_raw_conf) 127 | pred_prob = tf.sigmoid(conv_raw_prob) 128 | 129 | return tf.concat([pred_xywh, pred_conf, pred_prob], axis=-1) 130 | 131 | def focal(self, target, actual, alpha=1, gamma=2): 132 | focal_loss = alpha * tf.pow(tf.abs(target - actual), gamma) 133 | return focal_loss 134 | 135 | def bbox_giou(self, boxes1, boxes2): 136 | 137 | boxes1 = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5, 138 | boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1) 139 | boxes2 = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5, 140 | boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1) 141 | 142 | boxes1 = tf.concat([tf.minimum(boxes1[..., :2], boxes1[..., 2:]), 143 | tf.maximum(boxes1[..., :2], boxes1[..., 2:])], axis=-1) 144 | boxes2 = tf.concat([tf.minimum(boxes2[..., :2], boxes2[..., 2:]), 145 | tf.maximum(boxes2[..., :2], boxes2[..., 2:])], axis=-1) 146 | 147 | boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1]) 148 | boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1]) 149 | 150 | left_up = tf.maximum(boxes1[..., :2], boxes2[..., :2]) 151 | right_down = tf.minimum(boxes1[..., 2:], boxes2[..., 2:]) 152 | 153 | inter_section = tf.maximum(right_down - left_up, 0.0) 154 | inter_area = inter_section[..., 0] * inter_section[..., 1] 155 | union_area = boxes1_area + boxes2_area - inter_area 156 | iou = inter_area / union_area 157 | 158 | enclose_left_up = tf.minimum(boxes1[..., :2], boxes2[..., :2]) 159 | enclose_right_down = tf.maximum(boxes1[..., 2:], boxes2[..., 2:]) 160 | enclose = tf.maximum(enclose_right_down - enclose_left_up, 0.0) 161 | enclose_area = enclose[..., 0] * enclose[..., 1] 162 | giou = iou - 1.0 * (enclose_area - union_area) / enclose_area 163 | 164 | return giou 165 | 166 | def bbox_iou(self, boxes1, boxes2): 167 | 168 | boxes1_area = boxes1[..., 2] * boxes1[..., 3] 169 | boxes2_area = boxes2[..., 2] * boxes2[..., 3] 170 | 171 | boxes1 = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5, 172 | boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1) 173 | boxes2 = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5, 174 | boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1) 175 | 176 | left_up = tf.maximum(boxes1[..., :2], boxes2[..., :2]) 177 | right_down = tf.minimum(boxes1[..., 2:], boxes2[..., 2:]) 178 | 179 | inter_section = tf.maximum(right_down - left_up, 0.0) 180 | inter_area = inter_section[..., 0] * inter_section[..., 1] 181 | union_area = boxes1_area + boxes2_area - inter_area 182 | iou = 1.0 * inter_area / union_area 183 | 184 | return iou 185 | 186 | def loss_layer(self, conv, pred, label, bboxes, anchors, stride): 187 | 188 | conv_shape = tf.shape(conv) 189 | batch_size = conv_shape[0] 190 | output_size = conv_shape[1] 191 | input_size = stride * output_size 192 | conv = tf.reshape(conv, (batch_size, output_size, output_size, 193 | self.anchor_per_scale, 5 + self.num_class)) 194 | conv_raw_conf = conv[:, :, :, :, 4:5] 195 | conv_raw_prob = conv[:, :, :, :, 5:] 196 | 197 | pred_xywh = pred[:, :, :, :, 0:4] 198 | pred_conf = pred[:, :, :, :, 4:5] 199 | 200 | label_xywh = label[:, :, :, :, 0:4] 201 | respond_bbox = label[:, :, :, :, 4:5] 202 | label_prob = label[:, :, :, :, 5:] 203 | 204 | giou = tf.expand_dims(self.bbox_giou(pred_xywh, label_xywh), axis=-1) 205 | input_size = tf.cast(input_size, tf.float32) 206 | 207 | bbox_loss_scale = 2.0 - 1.0 * label_xywh[:, :, :, :, 2:3] * label_xywh[:, :, :, :, 3:4] / (input_size ** 2) 208 | giou_loss = respond_bbox * bbox_loss_scale * (1- giou) 209 | 210 | iou = self.bbox_iou(pred_xywh[:, :, :, :, np.newaxis, :], bboxes[:, np.newaxis, np.newaxis, np.newaxis, :, :]) 211 | max_iou = tf.expand_dims(tf.reduce_max(iou, axis=-1), axis=-1) 212 | 213 | respond_bgd = (1.0 - respond_bbox) * tf.cast( max_iou < self.iou_loss_thresh, tf.float32 ) 214 | 215 | conf_focal = self.focal(respond_bbox, pred_conf) 216 | 217 | conf_loss = conf_focal * ( 218 | respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits(labels=respond_bbox, logits=conv_raw_conf) 219 | + 220 | respond_bgd * tf.nn.sigmoid_cross_entropy_with_logits(labels=respond_bbox, logits=conv_raw_conf) 221 | ) 222 | 223 | prob_loss = respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits(labels=label_prob, logits=conv_raw_prob) 224 | 225 | giou_loss = tf.reduce_mean(tf.reduce_sum(giou_loss, axis=[1,2,3,4])) 226 | conf_loss = tf.reduce_mean(tf.reduce_sum(conf_loss, axis=[1,2,3,4])) 227 | prob_loss = tf.reduce_mean(tf.reduce_sum(prob_loss, axis=[1,2,3,4])) 228 | 229 | return giou_loss, conf_loss, prob_loss 230 | 231 | 232 | 233 | def compute_loss(self, label_sbbox, label_mbbox, label_lbbox, true_sbbox, true_mbbox, true_lbbox): 234 | 235 | with tf.name_scope('smaller_box_loss'): 236 | loss_sbbox = self.loss_layer(self.conv_sbbox, self.pred_sbbox, label_sbbox, true_sbbox, 237 | anchors = self.anchors[0], stride = self.strides[0]) 238 | 239 | with tf.name_scope('medium_box_loss'): 240 | loss_mbbox = self.loss_layer(self.conv_mbbox, self.pred_mbbox, label_mbbox, true_mbbox, 241 | anchors = self.anchors[1], stride = self.strides[1]) 242 | 243 | with tf.name_scope('bigger_box_loss'): 244 | loss_lbbox = self.loss_layer(self.conv_lbbox, self.pred_lbbox, label_lbbox, true_lbbox, 245 | anchors = self.anchors[2], stride = self.strides[2]) 246 | 247 | with tf.name_scope('giou_loss'): 248 | giou_loss = loss_sbbox[0] + loss_mbbox[0] + loss_lbbox[0] 249 | 250 | with tf.name_scope('conf_loss'): 251 | conf_loss = loss_sbbox[1] + loss_mbbox[1] + loss_lbbox[1] 252 | 253 | with tf.name_scope('prob_loss'): 254 | prob_loss = loss_sbbox[2] + loss_mbbox[2] + loss_lbbox[2] 255 | 256 | return giou_loss, conf_loss, prob_loss 257 | 258 | 259 | -------------------------------------------------------------------------------- /static/css/font-awesome.min.css: -------------------------------------------------------------------------------- 1 | /*! 2 | * 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